Representation Learning for Point Cloud Understanding

2025-12-05

AI Summary

This work addresses the limited representational capacity of point cloud learning by proposing a cross-modal representation learning framework that incorporates 2D visual priors. Methodologically, it innovatively leverages pre-trained 2D vision models to guide 3D point cloud network training via feature alignment—enabling effective 2D→3D knowledge transfer without naïve modality conversion. The framework integrates a point cloud encoder-decoder architecture, self-supervised pre-training, and primitive-level supervised segmentation into a unified learning paradigm. Extensive experiments on ScanNet and S3DIS benchmarks demonstrate substantial improvements in point cloud segmentation and scene understanding performance, validating the efficacy of 2D semantic priors in enhancing 3D representation learning. The proposed approach establishes a scalable, multimodal representation learning pathway for efficient 3D perception in autonomous driving and robotics applications.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the rapid advancement of technology, 3D data acquisition and utilization have become increasingly prevalent across various fields, including computer vision, robotics, and geospatial analysis. 3D data, captured through methods such as 3D scanners, LiDARs, and RGB-D cameras, provides rich geometric, shape, and scale information. When combined with 2D images, 3D data offers machines a comprehensive understanding of their environment, benefiting applications like autonomous driving, robotics, remote sensing, and medical treatment. This dissertation focuses on three main areas: supervised representation learning for point cloud primitive segmentation, self-supervised learning methods, and transfer learning from 2D to 3D. Our approach, which integrates pre-trained 2D models to support 3D network training, significantly improves 3D understanding without merely transforming 2D data. Extensive experiments validate the effectiveness of our methods, showcasing their potential to advance point cloud representation learning by effectively integrating 2D knowledge.

Problem

• Supervised learning for point cloud primitive segmentation
• Self-supervised learning methods for point cloud understanding
• Transfer learning from 2D to 3D using pre-trained models

Innovation

• Integrates pre-trained 2D models for 3D network training
• Uses self-supervised learning methods for point clouds
• Applies transfer learning from 2D to 3D data